Data pre-processed and analyzied from chat logs and web logs by Marcus Collins.

Analysis based on R-Scripts from Jacob LaRiviere.

R-Markdown and plots from Mike Wise.

Initialization of libraries

library(tidyverse,quietly=T,warn.conflicts=F)
library(lubridate,quietly=T,warn.conflicts=F)
library(scales,quietly=T,warn.conflicts=F)
library(zoo,quietly=T,warn.conflicts=F)
library(lmtest,quietly=T,warn.conflicts=F)
library(sandwich,quietly=T,warn.conflicts=F)
library(gridExtra,quietly=T,warn.conflicts = F)
library(knitr,quietly=T,warn.conflicts = F)

Set random seeds, record start time and version.

set.seed(1234)
version <-0.1
versionstring <- sprintf("Version %.1f",version)

starttime <- Sys.time()
startfmttime <- sprintf(format(starttime, "%d %b %Y - %H:%M:%S"))

print(sprintf("%s created on %s",versionstring,startfmttime))
## [1] "Version 0.1 created on 24 Mar 2017 - 16:55:10"

Various constants

tztz <- "UTC"  # Apparently all of our time zones are UTC...
s2date <- function(strdate){
  return(as.POSIXct(strdate,tz=tztz))
}
firstday <- s2date("2015-01-01")  # we will count days from the first day in 2015

smcdates <- c("2016-08-17/red/0-30%","2016-09-1/red/30-50%","2016-09-07/red/50-100%")
xbxdates <- c("2016-10-11/purple/0-10%","2016-10-18/purple/10-30%","2016-11-01/purple/30-50%","2016-12-15/purple/50-90%",
              "2016-11-17/blue/content change")
totdates <- c(smcdates,xbxdates)
smcback <- "lightsteelblue1"
xbxback <- "darkseagreen2"
xabback <- "darkseagreen3"
totback <- "wheat"

fpath <- "../TorontoData"
sdate <- s2date("2016-06-01")
mdate <- s2date("2017-01-01")
edate <- s2date("2017-03-07")
verbose <- 2
justdoone <- F # for testing

Misc utility functions

crackdate <- function(datestr){
  sar <- unlist(strsplit(datestr,"/"))
  sdate <- sar[[1]]
  date <- s2date(sdate)
  val <- 0
  sval <- sar[[3]]
  levpart <- sar[[3]]
  pctpresent <- F
  if (grepl("%",levpart)){
    levpart <- gsub("%","",levpart)
    val <- as.numeric(unlist(strsplit(levpart,"-"))[[2]])
    sval <- gsub("-","_",levpart)
    pctpresent <- T
  }
  return(list(date=date,sdate=sdate,val=val,sval=sval,pctpresent=pctpresent))
}

addStepDateToVek <- function(dates,idx,dtvek,vvek){
  cd1 <- crackdate(dates[[idx]])
  if (!cd1$pctpresent){
    # if there is no % don't do anything
    return(vvek)
  }
  dt1 <- cd1$date
  if (idx<length(dates)){
    cd2 <- crackdate(dates[[idx+1]])
    dt2 <- cd2$date
  } else {
    dt2 <- max(dtvek)
  }
  #print("addstepdate")
  #print(dt1)
  #print(dt2)
  val <- cd1$val
  tochg <-  dt1<=dtvek & dtvek<= dt2
  vvek[ tochg ] <- val
  #print(sprintf("changed %d values to %d",sum(tochg),val))
  return(vvek)
}
getStepDates <- function(dates,dtvek){
 vvek <- rep(0,length(dtvek))
 for (i in 1:length(dates)){
   vvek <- addStepDateToVek(dates,i,dtvek,vvek)
 }
 return(vvek)
}
getSmcStepDates <- function(dtvek){
 return(getStepDates(smcdates,dtvek))
}
getXabStepDates <- function(dtvek){
 return(getStepDates(xbxdates,dtvek))
}

Plot functions

addVlinesAndText <- function(vlines,gp){
  if (is.null(vlines)) return(gp) # do nothing in this case
  
  # split the lines and convert to data.frame
  sar <- strsplit(vlines,"/")
  # the following reforms the date strings into a data.frame for geom_vline
  ldf <- data.frame(t(matrix(unlist(sar),length(sar[[1]]),length(sar)))) #tricky
  names(ldf) <- c("dt","clr","lab")
  ldf$dt <- s2date(ldf$dt)
  ldf$ndt <- as.numeric(ldf$dt)
  # add a newline to the front so as to display the text 
  # this keeps the text from writing on top of the vline
  
  ldf$lab <- paste0("\n",ldf$lab) 
  # now actually add the verticle lines and the text 
  gp <- gp + geom_vline(xintercept=ldf$ndt,color=ldf$clr) +
             annotate(geom="text",x=ldf$dt,y=0,label=ldf$lab,color=ldf$clr,hjust=0,angle=90,na.rm=T)
  return(gp)
}
addBackground <- function(backg,gp){
  if (is.null(backg)) return(gp) # do nothing in this case
  gp <- gp + theme(panel.background = element_rect(fill = backg))
  return(gp)
}
overdate <- function(ovdate,defdate){
  # date override
  rv <- defdate
  if (!is.null(ovdate)) { 
    rv <- ovdate
  }
  return(rv)
}
dailyplot <- function(ddf,x,y,mtit="",xlab="date",ylab=NULL,vlines=NULL,backg=NULL,series=NULL,ovsdate=NULL,ovedate=NULL){
  # Single series plot  with monthly breaks on the x-axis
  
  # override dates if needed
  dpsdate <- overdate(ovsdate,sdate)
  dpedate <- overdate(ovedate,edate)
  
  brkctrl <- "1 month"
  dltdays <- difftime(dpedate,dpsdate,"days") 
  if (dltdays<30) brkctrl <- "1 day"
  
  gp <- ggplot(ddf,aes_string(x=x,y=y)) + 
             geom_line(aes_string(color=series),na.rm=T)  +
             xlab(xlab) + ylab(ylab) + ggtitle(mtit) +
             scale_x_datetime("Date",breaks = date_breaks(brkctrl),limits=c(dpsdate,dpedate))

  gp <- addVlinesAndText(vlines,gp)

  gp <- addBackground(backg,gp)

  return(gp)
}
residplot <- function(ddf,x,y,mtit="",xlab="date",ylab=NULL,vlines=NULL,backg=NULL,series=NULL,ovsdate=NULL,ovedate=NULL){
  # Single series plot  with monthly breaks on the x-axis
  
  # override dates if needed
  dpsdate <- overdate(ovsdate,sdate)
  dpedate <- overdate(ovedate,edate)
  
  brkctrl <- "1 month"
  dltdays <- difftime(dpedate,dpsdate,"days") 
  if (dltdays<30) brkctrl <- "1 day"
  
  gp <- ggplot(ddf,aes_string(x=x,y=y)) + 
             geom_point(aes_string(color=series),na.rm=T)  +
             xlab(xlab) + ylab(ylab) + ggtitle(mtit) +
             scale_x_datetime("Date",breaks = date_breaks(brkctrl),limits=c(dpsdate,dpedate)) +
             theme(axis.text.x = element_text(angle = 30, hjust = 1))

  gp <- addVlinesAndText(vlines,gp)

  gp <- addBackground(backg,gp)
  
  hp <- ggplot(ddf) + geom_histogram(aes_string(x=y),bins=30)
  hp <- addBackground(backg,hp)
  
  ghp <- grid.arrange(gp, hp, ncol=2,widths = c(2,1))

  return(ghp)
}

Read in consolidated chat, call, and session volume data

stload <- Sys.time()
tfname <- sprintf("%s/%s",fpath,"colsolidatedTorontoData01.csv")
condf <- read.csv(tfname)

minsessfilt <- 5000
nbef <- nrow(condf)
condf <- condf %>% filter(minsessfilt<actsess)
naft <- nrow(condf)
print(sprintf("Filtered %d of %d hours because sessions less than %d",(nbef-naft),naft,minsessfilt))
## [1] "Filtered 9 of 5815 hours because sessions less than 5000"
condf <- condf %>% mutate( dt = as.POSIXct(dt,tz=tztz) ) %>%
                   mutate( log_winchib = log(winchib) ) %>%
                   mutate( log_wincall = log(wincall) ) %>%
                   mutate( log_xbxchib = log(xbxchib) ) %>%
                   mutate( log_xbxcall = log(xbxcall) ) %>%
                   mutate( rate_winchib = winchib/actsess ) %>%
                   mutate( rate_wincall = wincall/actsess ) %>%
                   mutate( rate_xbxchib = xbxchib/actsess ) %>%
                   mutate( rate_xbxcall = xbxcall/actsess ) 

dcondf <- condf %>% group_by(dnum) %>% summarise(dt=min(dt),
                                                 totchib=sum(totchib),winchib=sum(winchib),xbxchib=sum(xbxchib),
                                                 totcall=sum(totcall),wincall=sum(wincall),xbxcall=sum(xbxcall),
                                                 actsess=sum(actsess),actuser=sum(actuser)
                                                 )

elap <- as.numeric((Sys.time()-stload)[1],units="secs")
print(sprintf("Loading consolidated data took %.1f secs",elap))
## [1] "Loading consolidated data took 0.1 secs"

chats

if (verbose>=2){
  pltdf <- dcondf %>% gather(series,chib,-dt) %>% filter(series %in% c("totchib","winchib","xbxchib"))
  dailyplot(pltdf,"dt","chib",series="series",mtit="Chats In Block",ylab="Sum",vlines=totdates,backg=totback)
}

# calls

if (verbose>=2){
  pltdf <- dcondf %>% gather(series,call,-dt) %>% filter(series %in% c("totcall","wincall","xbxcall"))
  dailyplot(pltdf,"dt","call",series="series",mtit="Calls",ylab="Sum",vlines=totdates,backg=totback)
}

# sessions

if (verbose>=2){
  pltdf <- dcondf %>% gather(series,active,-dt) %>% filter(series %in% c("actsess","actuser"))
  dailyplot(pltdf,"dt","active",series="series",mtit="Sessions",ylab="Sum",vlines=totdates,backg=totback)
}

regression code

results1 <- list()
results2 <- list()
initResults <- function(){
  results1 <<- list()
  results2 <<- list()
}
  
addToResults1 <- function(newresults){
  results1[[length(results1)+1]] <<- newresults
}
addToResults2 <- function(newresults){
  results2[[length(results2)+1]] <<- newresults
}
listtodf <- function(lst){
  nr <- length(lst)    # rows
  if (nr==0) return(NULL)
  nvek <- names(lst[[1]])
  nc <- length(nvek)   # columns
  df <- data.frame(idx=1:nr) # preallocate length
  for (i in 1:nc){
    iname <- nvek[i]
    df[[iname]] <- sapply(lst,`[[`,iname)
  }
  return(df)
}
getResults1 <- function(){
  return(listtodf(results1))
}
getResults2 <- function(){
  return(listtodf(results2))
}

getregdf <- function(df,vname,discdate,befdays,aftdays,dates){
  
  # filter on time
  sregdate <- discdate - days(befdays)
  eregdate <- discdate + days(aftdays)
  df <- df %>% filter( sregdate <= dt & dt <= eregdate) 
  
  df <- df %>% mutate( hour=as.factor(hour(dt)) ) %>%
               mutate( dow=as.factor(wday(dt))) %>%  
               mutate( idx=1:nrow(df) ) 

  # add level variables
  cutidx <- which(df$dt==discdate)

    df <- df %>% mutate( lin1=if_else(idx<cutidx,idx-cutidx+1L,0L)) %>%
                 mutate( lin2=if_else(idx<cutidx,0L,idx-cutidx+1L)) %>%
                 mutate( lin0=1 ) %>%
                 mutate( postchange=if_else(idx<cutidx,0L,1L) )

  return(df)
}
formlist = list(
  formel1="%s ~ hour + dow + lin1 + lin2 + postchange",
  formel2a="%s ~ hour * dow - 1",
  formel2b="%s ~ lin1 + lin2 + postchange"
#  formel2b="%s ~ lin0 + postchange"
)
doregression <- function(df,vname,formel,chgdate,befdays,aftdays){
  df <- getregdf(df,vname,chgdate,befdays,aftdays,dates)
  formstr <- sprintf(formlist[[formel]],vname)
  form <- as.formula(formstr)
  fit <- glm(form,data=df)
  summary_fit <- summary(fit)
  coeftest_fit <- coeftest(fit, vcov = vcovHC(fit, "HC1"))
  if (verbose>0){
    print(summary_fit)
    print(coeftest_fit)
  }
  df$resid <- resid(fit)
  df$predicted <-  df[[vname]]-df$resid
  # for debugging
  ffit <<- fit
  sfit <<- summary_fit
  cfit <<- coeftest_fit
  return(list(df=df,formstr=formstr,fit=fit,sfit=summary_fit,cfit=coeftest_fit))
}
regress1 <- function(df,vname,area,backg,dates,chgdate,mtit1,befdays=7,aftdays=7,model="model1"){
  crk <- crackdate(chgdate)
  idx <- length(results1)+1
  
  print(sprintf("%d Regression for %s %s on %s",idx,vname,model,chgdate))

  rv <- doregression(df,vname,"formel1",crk$date,befdays,aftdays)
  if (verbose>0){
    df1 <- rv$df %>%  gather_("series",vname,c(vname,"predicted"))
    mtit <- sprintf("%d - %s - %s",idx,mtit1,rv$formstr)
    plt <- dailyplot(df1,"dt",vname,series="series",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
    print(plt)
    mtit <- sprintf("%d - Residuals - %s - %s",idx,mtit1,rv$formstr)
    plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
  }
  newresults <- list(var=vname,
                     title=mtit1,
                     chgDate=crk$sdate,
                     chgVal=crk$sval,
                     model=model,
                     formula=rv$formstr,
                     chgCoef.fit=rv$fit$coefficients["postchange"],
                     chgCoef.smry=rv$sfit$coefficients["postchange",1],
                     chgStd.smry=rv$sfit$coefficients["postchange",2],
                     chgTval.smry=rv$sfit$coefficients["postchange",3],
                     chgPval.smry=rv$sfit$coefficients["postchange",4]
                     )
  addToResults1(newresults)
}
regress2 <- function(df,vname,area,backg,dates,chgdate,mtit1,befdays=7,aftdays=7,model="model2"){
  crk <- crackdate(chgdate)
  idx <- length(results1)+1
  
  print(sprintf("%d Regression for %s %s on %s",idx,vname,model,chgdate))

  # step 1
  rv1 <- doregression(df,vname,"formel2b",crk$date,befdays,aftdays)
  if (verbose>0){
    df1 <- rv1$df %>%  gather_("series",vname,c(vname,"predicted"))
    mtit <- sprintf("%d - %s - %s",idx,mtit1,rv1$formstr)
    plt <- dailyplot(df1,"dt",vname,series="series",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
    print(plt)
    mtit <- sprintf("%d - Step 1 Residuals - %s - %s",idx,mtit1,rv1$formstr)
    plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
  }
  newresults <- list(var=vname,
                     title=mtit1,
                     chgDate=crk$sdate,
                     chgVal=crk$sval,
                     model=model,
                     formula=rv1$formstr,
                     aic=rv1$fit$aic,
                     deviance=rv1$fit$dev
                     )
  addToResults1(newresults)
  
  # step 2
  df2 <- rv1$df
  df2$step1residuals <- rv1$fit$residuals
  vname2 <- "step1residuals"
  rv2 <- doregression(df2,vname2,"formel2b",crk$date,7,7)
  if (verbose>0){
    df1 <- rv2$df %>%  gather_("series",vname2,c(vname2,"predicted"))
    mtit <- sprintf("%d - %s - %s",idx,mtit1,rv2$formstr)
    plt <- dailyplot(df1,"dt",vname2,series="series",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
    print(plt)
    mtit <- sprintf("%d - Step 2 Residuals - %s - %s",idx,mtit1,rv2$formstr)
    plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
                     vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
  }

  newresults <- list(var=vname,
                     title=mtit1,
                     chgDate=crk$sdate,
                     chgVal=crk$sval,
                     model=model,
                     formula=rv2$formstr,
                     chgCoef.fit=rv2$fit$coefficients["postchange"],
                     chgCoef.smry=rv2$sfit$coefficients["postchange",1],
                     chgStd.smry=rv2$sfit$coefficients["postchange",2],
                     chgTval.smry=rv2$sfit$coefficients["postchange",3],
                     chgPval.smry=rv2$sfit$coefficients["postchange",4]
                     )
  addToResults2(newresults)
}
regressoverdates <- function(model,df,vname,area,title,befdays=7,aftdays=7){
  if (model=="model2" & grepl("^log",vname)) return(NULL)  # model2 is pointless for logs
  if (area=="SMC"){
    dates <- smcdates
    backg <- smcback
  } else {
    dates <- xbxdates
    backg <- xbxback
  }
  atit <- sprintf("%s - %s",area,title)
  for (chgdate in dates){
    if (model=="model1"){
      regress1(condf,vname,area,backg,dates,chgdate,atit,befdays=7,aftdays=7) 
    } else {
      regress2(condf,vname,area,backg,dates,chgdate,atit,befdays=28,aftdays=28) 
    }
  }
}
initResults()

justdoone <- F # for testing
verbose <- 2

m <- "model2"
if (justdoone){
  regressoverdates(m,condf,"winchib","SMC","Hourly Chats")
#  regressoverdates(m,condf,"winchib","Xbox","Hourly Chats")
} else {
  regressoverdates(m,condf,"winchib","SMC","Hourly Chats")
  regressoverdates(m,condf,"log_winchib","SMC","log(Hourly Chats)")
  regressoverdates(m,condf,"rate_winchib","SMC","Hourly Chat Rate per Session")
  
  regressoverdates(m,condf,"wincall","SMC","Hourly Chats")
  regressoverdates(m,condf,"log_wincall","SMC","log(Hourly Chats)")
  regressoverdates(m,condf,"rate_wincall","SMC","Hourly Chat Rate per Session")
  
  regressoverdates(m,condf,"xbxchib","Xbox","Hourly Chats")
  regressoverdates(m,condf,"log_xbxchib","Xbox","log(Hourly Chats)")
  regressoverdates(m,condf,"rate_xbxchib","Xbox","Hourly Chat Rate per Session")
  
  regressoverdates(m,condf,"xbxcall","Xbox","Hourly Chats")
  regressoverdates(m,condf,"log_xbxcall","Xbox","log(Hourly Chats)")
  regressoverdates(m,condf,"rate_xbxcall","Xbox","Hourly Chat Rate per Session")
}
## [1] "1 Regression for winchib model2 on 2016-08-17/red/0-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -329.56   -92.35   -10.22    88.00   344.57  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 337.53214    8.45516  39.920  < 2e-16 ***
## lin1          0.14941    0.02182   6.848 1.13e-11 ***
## lin2         -0.01278    0.02177  -0.587   0.5573    
## postchange  -21.19718   11.96630  -1.771   0.0767 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12037.11)
## 
##     Null deviance: 16914320  on 1344  degrees of freedom
## Residual deviance: 16141763  on 1341  degrees of freedom
## AIC: 16460
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 337.53214    9.10096 37.0875 < 2.2e-16 ***
## lin1          0.14941    0.02096  7.1284 1.015e-12 ***
## lin2         -0.01278    0.02198 -0.5814   0.56096    
## postchange  -21.19718   12.46115 -1.7011   0.08893 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -308.35  -101.72   -20.54    93.90   301.10  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.4902    17.9232  -1.590    0.113
## lin1         -0.1820     0.1856  -0.980    0.328
## lin2          0.2122     0.1840   1.154    0.250
## postchange    9.6937    25.4228   0.381    0.703
## 
## (Dispersion parameter for gaussian family taken to be 13612.98)
## 
##     Null deviance: 4577565  on 336  degrees of freedom
## Residual deviance: 4533122  on 333  degrees of freedom
## AIC: 4170.2
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -28.49022   20.47929 -1.3912   0.1642
## lin1         -0.18199    0.21927 -0.8300   0.4066
## lin2          0.21222    0.18952  1.1198   0.2628
## postchange    9.69372   27.25017  0.3557   0.7220

## [1] "2 Regression for winchib model2 on 2016-09-1/red/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -311.79  -106.43    -7.10    95.99   364.60  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 319.95367    9.18342  34.840  < 2e-16 ***
## lin1          0.01540    0.02370   0.650   0.5159    
## lin2          0.10116    0.02364   4.279 2.01e-05 ***
## postchange  -29.49736   12.99698  -2.270   0.0234 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 14199.96)
## 
##     Null deviance: 19340147  on 1344  degrees of freedom
## Residual deviance: 19042152  on 1341  degrees of freedom
## AIC: 16682
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 319.953668   8.418529 38.0059 < 2.2e-16 ***
## lin1          0.015399   0.021616  0.7124 0.4762193    
## lin2          0.101163   0.026290  3.8480 0.0001191 ***
## postchange  -29.497355  12.580011 -2.3448 0.0190383 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -180.396  -104.618    -3.042    96.365   267.788  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  24.0708    17.1706   1.402   0.1619  
## lin1          0.3696     0.1778   2.078   0.0385 *
## lin2          0.1533     0.1762   0.870   0.3851  
## postchange  -19.8954    24.3552  -0.817   0.4146  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12493.7)
## 
##     Null deviance: 4272312  on 336  degrees of freedom
## Residual deviance: 4160403  on 333  degrees of freedom
## AIC: 4141.3
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  24.07078   16.54180  1.4551  0.14563  
## lin1          0.36955    0.17766  2.0801  0.03752 *
## lin2          0.15329    0.20499  0.7478  0.45459  
## postchange  -19.89543   25.94392 -0.7669  0.44316  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "3 Regression for winchib model2 on 2016-09-07/red/50-100%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -310.56  -111.19    -6.44    99.84   368.41  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 314.340188   9.366936  33.558  < 2e-16 ***
## lin1          0.003276   0.024170   0.136    0.892    
## lin2          0.109634   0.024116   4.546 5.96e-06 ***
## postchange  -17.344672  13.256708  -1.308    0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 14773.16)
## 
##     Null deviance: 20260491  on 1344  degrees of freedom
## Residual deviance: 19810810  on 1341  degrees of freedom
## AIC: 16736
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept) 314.3401879   8.7945614 35.7426 < 2.2e-16 ***
## lin1          0.0032761   0.0233650  0.1402    0.8885    
## lin2          0.1096336   0.0250691  4.3733 1.224e-05 ***
## postchange  -17.3446720  12.7163480 -1.3640    0.1726    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -189.459  -111.550    -4.187   100.050   249.433  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.8945    17.4735  -0.623    0.533
## lin1         -0.1153     0.1810  -0.637    0.524
## lin2         -0.2278     0.1794  -1.270    0.205
## postchange   30.8848    24.7849   1.246    0.214
## 
## (Dispersion parameter for gaussian family taken to be 12938.43)
## 
##     Null deviance: 4334930  on 336  degrees of freedom
## Residual deviance: 4308498  on 333  degrees of freedom
## AIC: 4153
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.89450   18.25190 -0.5969   0.5506
## lin1         -0.11535    0.19308 -0.5974   0.5502
## lin2         -0.22781    0.18932 -1.2033   0.2289
## postchange   30.88482   26.08501  1.1840   0.2364

## [1] "4 Regression for rate_winchib model2 on 2016-08-17/red/0-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.630e-03  -5.747e-04  -1.389e-05   5.588e-04   2.294e-03  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.680e-03  5.960e-05  44.964  < 2e-16 ***
## lin1         7.038e-07  1.538e-07   4.576 5.17e-06 ***
## lin2         1.115e-07  1.535e-07   0.726    0.468    
## postchange  -6.945e-05  8.436e-05  -0.823    0.410    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5.981914e-07)
## 
##     Null deviance: 0.00082905  on 1344  degrees of freedom
## Residual deviance: 0.00080217  on 1341  degrees of freedom
## AIC: -15450
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.6801e-03  5.9541e-05 45.0125 < 2.2e-16 ***
## lin1         7.0385e-07  1.4167e-07  4.9681 6.762e-07 ***
## lin2         1.1146e-07  1.6138e-07  0.6907    0.4898    
## postchange  -6.9449e-05  8.4614e-05 -0.8208    0.4118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.500e-03  -5.791e-04   2.867e-05   5.534e-04   2.012e-03  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.551e-04  1.194e-04  -1.299    0.195
## lin1        -6.290e-07  1.237e-06  -0.509    0.611
## lin2         6.010e-07  1.226e-06   0.490    0.624
## postchange   1.103e-04  1.694e-04   0.651    0.516
## 
## (Dispersion parameter for gaussian family taken to be 6.045205e-07)
## 
##     Null deviance: 0.00020260  on 336  degrees of freedom
## Residual deviance: 0.00020131  on 333  degrees of freedom
## AIC: -3863.1
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)
## (Intercept) -1.5515e-04  1.2441e-04 -1.2470   0.2124
## lin1        -6.2900e-07  1.2376e-06 -0.5083   0.6113
## lin2         6.0103e-07  1.1572e-06  0.5194   0.6035
## postchange   1.1027e-04  1.6646e-04  0.6624   0.5077

## [1] "5 Regression for rate_winchib model2 on 2016-09-1/red/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.572e-03  -6.353e-04  -3.224e-05   6.126e-04   2.451e-03  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.598e-03  6.300e-05  41.238   <2e-16 ***
## lin1         1.002e-08  1.626e-07   0.062    0.951    
## lin2        -7.335e-08  1.622e-07  -0.452    0.651    
## postchange   7.843e-05  8.916e-05   0.880    0.379    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 6.682508e-07)
## 
##     Null deviance: 0.00089736  on 1344  degrees of freedom
## Residual deviance: 0.00089612  on 1341  degrees of freedom
## AIC: -15301
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  2.5979e-03  5.9582e-05 43.6030   <2e-16 ***
## lin1         1.0018e-08  1.5323e-07  0.0654   0.9479    
## lin2        -7.3354e-08  1.7438e-07 -0.4207   0.6740    
## postchange   7.8426e-05  8.7571e-05  0.8956   0.3705    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -1.948e-03  -6.086e-04  -6.147e-05   5.884e-04   1.983e-03  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.223e-06  1.231e-04   0.026    0.979
## lin1        7.640e-08  1.275e-06   0.060    0.952
## lin2        5.620e-07  1.263e-06   0.445    0.657
## postchange  4.117e-05  1.746e-04   0.236    0.814
## 
## (Dispersion parameter for gaussian family taken to be 6.420869e-07)
## 
##     Null deviance: 0.00021471  on 336  degrees of freedom
## Residual deviance: 0.00021381  on 333  degrees of freedom
## AIC: -3842.8
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.2231e-06 1.1050e-04  0.0292   0.9767
## lin1        7.6404e-08 1.2099e-06  0.0631   0.9496
## lin2        5.6202e-07 1.3595e-06  0.4134   0.6793
## postchange  4.1165e-05 1.6773e-04  0.2454   0.8061

## [1] "6 Regression for rate_winchib model2 on 2016-09-07/red/50-100%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.513e-03  -6.662e-04  -6.801e-05   6.466e-04   2.771e-03  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.761e-03  6.454e-05  42.781  < 2e-16 ***
## lin1         4.143e-07  1.665e-07   2.488  0.01297 *  
## lin2         4.391e-07  1.662e-07   2.643  0.00832 ** 
## postchange  -2.532e-04  9.134e-05  -2.772  0.00564 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 7.013206e-07)
## 
##     Null deviance: 0.00095009  on 1344  degrees of freedom
## Residual deviance: 0.00094047  on 1341  degrees of freedom
## AIC: -15236
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.7610e-03  6.1570e-05 44.8438 < 2.2e-16 ***
## lin1         4.1432e-07  1.5650e-07  2.6475  0.008109 ** 
## lin2         4.3908e-07  1.8046e-07  2.4331  0.014969 *  
## postchange  -2.5324e-04  9.0204e-05 -2.8074  0.004995 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -2.047e-03  -6.530e-04  -3.185e-05   6.318e-04   2.361e-03  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1.441e-04  1.259e-04   1.145    0.253
## lin1         1.482e-06  1.304e-06   1.137    0.256
## lin2        -1.379e-06  1.292e-06  -1.067    0.287
## postchange   5.149e-05  1.785e-04   0.288    0.773
## 
## (Dispersion parameter for gaussian family taken to be 6.713686e-07)
## 
##     Null deviance: 0.00022548  on 336  degrees of freedom
## Residual deviance: 0.00022357  on 333  degrees of freedom
## AIC: -3827.8
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)
## (Intercept)  1.4410e-04  1.4031e-04  1.0270   0.3044
## lin1         1.4825e-06  1.3660e-06  1.0853   0.2778
## lin2        -1.3790e-06  1.2547e-06 -1.0991   0.2717
## postchange   5.1489e-05  1.8743e-04  0.2747   0.7835

## [1] "7 Regression for wincall model2 on 2016-08-17/red/0-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -461.10  -265.39   -23.99   223.27  1089.67  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 354.14244   21.59879  16.396  < 2e-16 ***
## lin1         -0.23155    0.05573  -4.155 3.46e-05 ***
## lin2         -0.05600    0.05561  -1.007    0.314    
## postchange    2.36151   30.56803   0.077    0.938    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 78548.36)
## 
##     Null deviance: 109752259  on 1344  degrees of freedom
## Residual deviance: 105333345  on 1341  degrees of freedom
## AIC: 18983
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 354.142441  20.712629 17.0979 < 2.2e-16 ***
## lin1         -0.231550   0.059359 -3.9008 9.587e-05 ***
## lin2         -0.055997   0.048820 -1.1470    0.2514    
## postchange    2.361512  28.422672  0.0831    0.9338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -359.95  -265.17   -24.33   206.62   477.96  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  42.9870    39.8966   1.077    0.282
## lin1          0.5103     0.4132   1.235    0.218
## lin2          0.3148     0.4095   0.769    0.443
## postchange  -63.1475    56.5905  -1.116    0.265
## 
## (Dispersion parameter for gaussian family taken to be 67451.82)
## 
##     Null deviance: 22607490  on 336  degrees of freedom
## Residual deviance: 22461457  on 333  degrees of freedom
## AIC: 4709.5
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  42.98696   42.43070  1.0131   0.3110
## lin1          0.51033    0.44193  1.1548   0.2482
## lin2          0.31482    0.42743  0.7365   0.4614
## postchange  -63.14751   59.08883 -1.0687   0.2852

## [1] "8 Regression for wincall model2 on 2016-09-1/red/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -422.83  -252.59   -23.03   214.04   646.96  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 361.904214  20.144737  17.965  < 2e-16 ***
## lin1          0.007818   0.051980   0.150 0.880468    
## lin2          0.310346   0.051864   5.984 2.79e-09 ***
## postchange  -95.279943  28.510165  -3.342 0.000855 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 68328.44)
## 
##     Null deviance: 94124403  on 1344  degrees of freedom
## Residual deviance: 91628440  on 1341  degrees of freedom
## AIC: 18796
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept) 361.9042135  19.4633348 18.5942 < 2.2e-16 ***
## lin1          0.0078181   0.0500746  0.1561 0.8759318    
## lin2          0.3103464   0.0555125  5.5906 2.263e-08 ***
## postchange  -95.2799432  26.8441290 -3.5494 0.0003861 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -347.50  -224.38   -11.09   185.74   466.27  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  39.5168    36.2639   1.090   0.2766  
## lin1          0.6297     0.3756   1.677   0.0945 .
## lin2          0.1458     0.3722   0.392   0.6956  
## postchange  -30.3619    51.4377  -0.590   0.5554  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 55727.55)
## 
##     Null deviance: 18823387  on 336  degrees of freedom
## Residual deviance: 18557274  on 333  degrees of freedom
## AIC: 4645.2
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  39.51682   39.75431  0.9940   0.3202
## lin1          0.62967    0.40423  1.5577   0.1193
## lin2          0.14578    0.39691  0.3673   0.7134
## postchange  -30.36193   53.92056 -0.5631   0.5734

## [1] "9 Regression for wincall model2 on 2016-09-07/red/50-100%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -441.44  -260.31   -25.27   221.15   674.08  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 317.45007   20.70386  15.333  < 2e-16 ***
## lin1         -0.09387    0.05342  -1.757   0.0791 .  
## lin2          0.24597    0.05330   4.615 4.32e-06 ***
## postchange   -2.61066   29.30147  -0.089   0.9290    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 72174)
## 
##     Null deviance: 99345425  on 1344  degrees of freedom
## Residual deviance: 96785338  on 1341  degrees of freedom
## AIC: 18869
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 317.450068  18.253535 17.3912 < 2.2e-16 ***
## lin1         -0.093874   0.048988 -1.9163   0.05533 .  
## lin2          0.245974   0.055367  4.4426 8.888e-06 ***
## postchange   -2.610663  26.590701 -0.0982   0.92179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -331.76  -223.54   -24.72   175.40   505.89  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.9263    35.2945  -0.536    0.592
## lin1         -0.1019     0.3655  -0.279    0.781
## lin2          0.1673     0.3623   0.462    0.644
## postchange   -2.3886    50.0627  -0.048    0.962
## 
## (Dispersion parameter for gaussian family taken to be 52788)
## 
##     Null deviance: 17594703  on 336  degrees of freedom
## Residual deviance: 17578404  on 333  degrees of freedom
## AIC: 4626.9
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.92633   38.12619 -0.4964   0.6196
## lin1         -0.10188    0.40485 -0.2516   0.8013
## lin2          0.16735    0.39927  0.4191   0.6751
## postchange   -2.38856   53.51419 -0.0446   0.9644

## [1] "10 Regression for rate_wincall model2 on 2016-08-17/red/0-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.003736  -0.001733  -0.000002   0.001468   0.010237  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.506e-03  1.466e-04  17.100  < 2e-16 ***
## lin1        -2.593e-06  3.782e-07  -6.855 1.08e-11 ***
## lin2        -1.245e-07  3.774e-07  -0.330    0.741    
## postchange   1.314e-04  2.074e-04   0.633    0.527    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 3.617212e-06)
## 
##     Null deviance: 0.0052258  on 1344  degrees of freedom
## Residual deviance: 0.0048507  on 1341  degrees of freedom
## AIC: -13030
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.5063e-03  1.3290e-04 18.8583 < 2.2e-16 ***
## lin1        -2.5926e-06  4.0147e-07 -6.4578 1.062e-10 ***
## lin2        -1.2451e-07  3.1920e-07 -0.3901    0.6965    
## postchange   1.3140e-04  1.8077e-04  0.7269    0.4673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0025573  -0.0015822   0.0000865   0.0014456   0.0031873  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  4.280e-04  2.486e-04   1.721   0.0861 .
## lin1         4.811e-06  2.575e-06   1.869   0.0626 .
## lin2         1.200e-06  2.552e-06   0.470   0.6384  
## postchange  -4.794e-04  3.526e-04  -1.359   0.1749  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 2.619271e-06)
## 
##     Null deviance: 0.00088199  on 336  degrees of freedom
## Residual deviance: 0.00087222  on 333  degrees of freedom
## AIC: -3369
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)  
## (Intercept)  4.2795e-04  2.5159e-04  1.7010  0.08895 .
## lin1         4.8109e-06  2.5070e-06  1.9190  0.05498 .
## lin2         1.2005e-06  2.4847e-06  0.4831  0.62900  
## postchange  -4.7942e-04  3.5028e-04 -1.3687  0.17111  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "11 Regression for rate_wincall model2 on 2016-09-1/red/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0026911  -0.0016161   0.0000459   0.0013877   0.0103087  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.644e-03  1.275e-04  20.745  < 2e-16 ***
## lin1        -8.086e-08  3.289e-07  -0.246 0.805823    
## lin2         1.194e-06  3.281e-07   3.638 0.000285 ***
## postchange  -3.124e-04  1.804e-04  -1.732 0.083547 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 2.73536e-06)
## 
##     Null deviance: 0.0037058  on 1344  degrees of freedom
## Residual deviance: 0.0036681  on 1341  degrees of freedom
## AIC: -13405
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.6441e-03  1.2287e-04 21.5193 < 2.2e-16 ***
## lin1        -8.0862e-08  3.1581e-07 -0.2560 0.7979161    
## lin2         1.1938e-06  3.4954e-07  3.4153 0.0006371 ***
## postchange  -3.1239e-04  1.7827e-04 -1.7523 0.0797244 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0025323  -0.0015946  -0.0000797   0.0013247   0.0100585  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1.164e-04  2.594e-04   0.449    0.654
## lin1         1.910e-06  2.686e-06   0.711    0.477
## lin2         1.875e-06  2.662e-06   0.704    0.482
## postchange  -1.361e-04  3.679e-04  -0.370    0.712
## 
## (Dispersion parameter for gaussian family taken to be 2.850998e-06)
## 
##     Null deviance: 0.00095505  on 336  degrees of freedom
## Residual deviance: 0.00094938  on 333  degrees of freedom
## AIC: -3340.4
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)
## (Intercept)  1.1638e-04  2.4055e-04  0.4838   0.6285
## lin1         1.9103e-06  2.4354e-06  0.7844   0.4328
## lin2         1.8751e-06  2.9171e-06  0.6428   0.5204
## postchange  -1.3614e-04  3.3792e-04 -0.4029   0.6871

## [1] "12 Regression for rate_wincall model2 on 2016-09-07/red/50-100%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0028717  -0.0016597   0.0000482   0.0014252   0.0102239  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.588e-03  1.294e-04  20.001  < 2e-16 ***
## lin1        -1.891e-07  3.339e-07  -0.566 0.571254    
## lin2         1.252e-06  3.332e-07   3.759 0.000178 ***
## postchange  -1.824e-04  1.832e-04  -0.996 0.319488    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 2.820075e-06)
## 
##     Null deviance: 0.0038329  on 1344  degrees of freedom
## Residual deviance: 0.0037817  on 1341  degrees of freedom
## AIC: -13364
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.5884e-03  1.3847e-04 18.6927 < 2.2e-16 ***
## lin1        -1.8912e-07  3.4967e-07 -0.5409 0.5885991    
## lin2         1.2524e-06  3.3383e-07  3.7515 0.0001758 ***
## postchange  -1.8240e-04  1.8346e-04 -0.9942 0.3201061    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0026141  -0.0015578   0.0000846   0.0012651   0.0099778  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  2.461e-04  2.527e-04   0.974    0.331
## lin1         3.182e-06  2.617e-06   1.216    0.225
## lin2         1.308e-06  2.594e-06   0.504    0.614
## postchange  -3.447e-04  3.585e-04  -0.962    0.337
## 
## (Dispersion parameter for gaussian family taken to be 2.706322e-06)
## 
##     Null deviance: 0.00090598  on 336  degrees of freedom
## Residual deviance: 0.00090121  on 333  degrees of freedom
## AIC: -3358
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)
## (Intercept)  2.4610e-04  3.6261e-04  0.6787   0.4973
## lin1         3.1820e-06  3.4216e-06  0.9300   0.3524
## lin2         1.3083e-06  2.3620e-06  0.5539   0.5797
## postchange  -3.4468e-04  4.2724e-04 -0.8068   0.4198

## [1] "13 Regression for xbxchib model2 on 2016-10-11/purple/0-10%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -261.73  -114.81     0.72   103.88   340.75  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 237.84995   10.08577  23.583  < 2e-16 ***
## lin1         -0.10854    0.02642  -4.109 4.22e-05 ***
## lin2          0.02778    0.02589   1.073    0.283    
## postchange  -33.70708   14.23143  -2.368    0.018 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 16873.28)
## 
##     Null deviance: 23938381  on 1332  degrees of freedom
## Residual deviance: 22424584  on 1329  degrees of freedom
## AIC: 16764
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 237.849954   9.775393 24.3315 < 2.2e-16 ***
## lin1         -0.108543   0.028683 -3.7842 0.0001542 ***
## lin2          0.027780   0.023663  1.1740 0.2404107    
## postchange  -33.707080  13.052416 -2.5824 0.0098104 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -201.345   -95.963     0.902    92.236   295.222  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.79001   17.99739  -0.044   0.9650  
## lin1          0.08383    0.19823   0.423   0.6727  
## lin2          0.39759    0.17920   2.219   0.0272 *
## postchange  -22.08934   25.14643  -0.878   0.3804  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12916.14)
## 
##     Null deviance: 4265111  on 326  degrees of freedom
## Residual deviance: 4171912  on 323  degrees of freedom
## AIC: 4029.4
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  -0.790007  17.808799 -0.0444  0.96462  
## lin1          0.083829   0.208501  0.4021  0.68764  
## lin2          0.397592   0.170897  2.3265  0.01999 *
## postchange  -22.089343  23.789510 -0.9285  0.35313  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "14 Regression for xbxchib model2 on 2016-10-18/purple/10-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -259.49  -109.42     0.85    98.48   339.96  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 210.59009    9.59691  21.944  < 2e-16 ***
## lin1         -0.12342    0.02514  -4.910 1.03e-06 ***
## lin2          0.03684    0.02596   1.419    0.156    
## postchange  -10.09558   13.66101  -0.739    0.460    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 15277.22)
## 
##     Null deviance: 20847384  on 1309  degrees of freedom
## Residual deviance: 19952048  on 1306  degrees of freedom
## AIC: 16344
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 210.590094   9.387721 22.4325 < 2.2e-16 ***
## lin1         -0.123416   0.028106 -4.3911 1.128e-05 ***
## lin2          0.036835   0.024546  1.5007    0.1334    
## postchange  -10.095580  12.767907 -0.7907    0.4291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -198.650   -96.730     1.435    90.553   242.862  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  39.0204    16.9330   2.304   0.0218 * 
## lin1          0.5208     0.1754   2.970   0.0032 **
## lin2          0.1763     0.1769   0.996   0.3198   
## postchange  -44.0174    24.0911  -1.827   0.0686 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12150.36)
## 
##     Null deviance: 4158081  on 334  degrees of freedom
## Residual deviance: 4021769  on 331  degrees of freedom
## AIC: 4107.4
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  39.02043   17.59629  2.2175  0.02659 * 
## lin1          0.52080    0.17177  3.0320  0.00243 **
## lin2          0.17630    0.18367  0.9599  0.33712   
## postchange  -44.01743   24.67464 -1.7839  0.07444 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "15 Regression for xbxchib model2 on 2016-11-01/purple/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -221.21  -106.73     1.48    97.22   313.10  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 192.76212    9.13096  21.111  < 2e-16 ***
## lin1         -0.06898    0.02399  -2.876   0.0041 ** 
## lin2          0.10212    0.02455   4.160 3.38e-05 ***
## postchange    7.86980   12.97769   0.606   0.5443    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 13788.08)
## 
##     Null deviance: 18470543  on 1309  degrees of freedom
## Residual deviance: 18007237  on 1306  degrees of freedom
## AIC: 16210
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 192.762119   8.006706 24.0751 < 2.2e-16 ***
## lin1         -0.068984   0.022178 -3.1105  0.001868 ** 
## lin2          0.102121   0.027085  3.7704  0.000163 ***
## postchange    7.869798  12.664837  0.6214  0.534343    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -258.721  -104.999     0.911    89.538   262.169  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -9.6324    17.3911  -0.554   0.5800  
## lin1         -0.0803     0.1801  -0.446   0.6560  
## lin2          0.4241     0.1785   2.376   0.0181 *
## postchange   -6.2571    24.6680  -0.254   0.7999  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12816.63)
## 
##     Null deviance: 4387701  on 336  degrees of freedom
## Residual deviance: 4267937  on 333  degrees of freedom
## AIC: 4149.9
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -9.632435  14.765107 -0.6524  0.51416  
## lin1        -0.080304   0.162534 -0.4941  0.62125  
## lin2         0.424056   0.195317  2.1711  0.02992 *
## postchange  -6.257090  22.806075 -0.2744  0.78381  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "16 Regression for xbxchib model2 on 2016-12-15/purple/50-90%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -260.51  -131.51    -7.33    98.77   581.93  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 203.53260   11.65075  17.469  < 2e-16 ***
## lin1         -0.07488    0.03006  -2.491   0.0129 *  
## lin2          0.03752    0.03000   1.251   0.2113    
## postchange   79.65488   16.48891   4.831 1.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 22855.25)
## 
##     Null deviance: 32343744  on 1344  degrees of freedom
## Residual deviance: 30648889  on 1341  degrees of freedom
## AIC: 17323
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 203.532597   8.399446 24.2317 < 2.2e-16 ***
## lin1         -0.074884   0.022612 -3.3117 0.0009272 ***
## lin2          0.037517   0.028041  1.3379 0.1809185    
## postchange   79.654877  14.413105  5.5266 3.266e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -202.436  -109.211     6.105    94.351   260.782  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  26.7977    17.2869   1.550   0.1220    
## lin1          0.4279     0.1790   2.390   0.0174 *  
## lin2          0.2258     0.1774   1.273   0.2040    
## postchange  -98.4643    24.5202  -4.016 7.33e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12663.53)
## 
##     Null deviance: 4469535  on 336  degrees of freedom
## Residual deviance: 4216957  on 333  degrees of freedom
## AIC: 4145.8
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value  Pr(>|z|)    
## (Intercept)  26.79771   15.90717  1.6846  0.092060 .  
## lin1          0.42786    0.15705  2.7244  0.006443 ** 
## lin2          0.22583    0.17616  1.2819  0.199862    
## postchange  -98.46426   23.59437 -4.1732 3.003e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "17 Regression for xbxchib model2 on 2016-11-17/blue/content change"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -212.17  -110.35     0.04    95.14   339.02  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 221.86927    9.25248  23.979  < 2e-16 ***
## lin1          0.02971    0.02480   1.198  0.23113    
## lin2         -0.07336    0.02338  -3.138  0.00174 ** 
## postchange   31.64215   12.97246   2.439  0.01485 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 13879.3)
## 
##     Null deviance: 18511758  on 1319  degrees of freedom
## Residual deviance: 18265158  on 1316  degrees of freedom
## AIC: 16342
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 221.869268   9.388595 23.6318 < 2.2e-16 ***
## lin1          0.029708   0.024019  1.2368  0.216149    
## lin2         -0.073356   0.022567 -3.2506  0.001152 ** 
## postchange   31.642154  13.274200  2.3837  0.017138 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -207.13  -115.09     6.48   104.16   277.10  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   4.8527    19.6549   0.247    0.805
## lin1          0.1087     0.2360   0.460    0.646
## lin2          0.3091     0.1876   1.648    0.100
## postchange  -43.4400    26.9109  -1.614    0.107
## 
## (Dispersion parameter for gaussian family taken to be 14149.28)
## 
##     Null deviance: 4434521  on 313  degrees of freedom
## Residual deviance: 4386278  on 310  degrees of freedom
## AIC: 3898.1
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   4.85270   18.79890  0.2581  0.79630  
## lin1          0.10867    0.21665  0.5016  0.61595  
## lin2          0.30912    0.17483  1.7681  0.07704 .
## postchange  -43.43999   25.39479 -1.7106  0.08716 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "18 Regression for rate_xbxchib model2 on 2016-10-11/purple/0-10%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.002199  -0.001322  -0.000414   0.000980   0.050981  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.168e-03  1.815e-04  11.946   <2e-16 ***
## lin1        -5.476e-07  4.754e-07  -1.152    0.250    
## lin2         4.033e-07  4.659e-07   0.866    0.387    
## postchange  -1.749e-04  2.561e-04  -0.683    0.495    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5.46381e-06)
## 
##     Null deviance: 0.0072889  on 1332  degrees of freedom
## Residual deviance: 0.0072614  on 1329  degrees of freedom
## AIC: -12364
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  2.1680e-03  1.0759e-04 20.1504  < 2e-16 ***
## lin1        -5.4758e-07  3.0442e-07 -1.7987  0.07206 .  
## lin2         4.0333e-07  3.4675e-07  1.1632  0.24477    
## postchange  -1.7486e-04  2.2346e-04 -0.7825  0.43391    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0020813  -0.0010879  -0.0002986   0.0010626   0.0036961  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  3.142e-04  2.012e-04   1.561  0.11940   
## lin1         4.396e-06  2.217e-06   1.983  0.04819 * 
## lin2         5.723e-06  2.004e-06   2.856  0.00456 **
## postchange  -9.049e-04  2.812e-04  -3.218  0.00142 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.614804e-06)
## 
##     Null deviance: 0.00054155  on 326  degrees of freedom
## Residual deviance: 0.00052158  on 323  degrees of freedom
## AIC: -3427
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  3.1422e-04  2.1398e-04  1.4684 0.1419854    
## lin1         4.3958e-06  2.2546e-06  1.9497 0.0512114 .  
## lin2         5.7232e-06  1.9198e-06  2.9811 0.0028723 ** 
## postchange  -9.0488e-04  2.6856e-04 -3.3694 0.0007533 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "19 Regression for rate_xbxchib model2 on 2016-10-18/purple/10-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.002104  -0.001277  -0.000409   0.000932   0.050812  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.993e-03  1.795e-04  11.099   <2e-16 ***
## lin1        -5.292e-07  4.703e-07  -1.125    0.261    
## lin2        -5.384e-07  4.856e-07  -1.109    0.268    
## postchange   3.243e-04  2.556e-04   1.269    0.205    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5.347008e-06)
## 
##     Null deviance: 0.0069967  on 1309  degrees of freedom
## Residual deviance: 0.0069832  on 1306  degrees of freedom
## AIC: -12178
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  1.9927e-03  1.0387e-04 19.1852  < 2e-16 ***
## lin1        -5.2921e-07  2.8911e-07 -1.8305  0.06718 .  
## lin2        -5.3836e-07  7.4513e-07 -0.7225  0.46998    
## postchange   3.2427e-04  3.5859e-04  0.9043  0.36584    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.002779  -0.001341  -0.000503   0.000689   0.050402  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  4.145e-04  5.992e-04   0.692    0.490
## lin1         6.435e-06  6.205e-06   1.037    0.301
## lin2         7.625e-06  6.261e-06   1.218    0.224
## postchange  -7.128e-04  8.525e-04  -0.836    0.404
## 
## (Dispersion parameter for gaussian family taken to be 1.521393e-05)
## 
##     Null deviance: 0.0050928  on 334  degrees of freedom
## Residual deviance: 0.0050358  on 331  degrees of freedom
## AIC: -2759.6
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  4.1446e-04  2.1081e-04  1.9660 0.0492937 *  
## lin1         6.4346e-06  1.9414e-06  3.3144 0.0009183 ***
## lin2         7.6250e-06  2.8583e-06  2.6677 0.0076371 ** 
## postchange  -7.1284e-04  4.0527e-04 -1.7589 0.0785942 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "20 Regression for rate_xbxchib model2 on 2016-11-01/purple/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.002321  -0.001254  -0.000377   0.000912   0.050961  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.117e-03  1.807e-04  11.717  < 2e-16 ***
## lin1        -4.501e-09  4.747e-07  -0.009  0.99244    
## lin2         1.417e-06  4.857e-07   2.918  0.00358 ** 
## postchange  -3.060e-04  2.568e-04  -1.192  0.23363    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5.399112e-06)
## 
##     Null deviance: 0.0071050  on 1309  degrees of freedom
## Residual deviance: 0.0070512  on 1306  degrees of freedom
## AIC: -12166
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  2.1172e-03  2.0732e-04 10.2119 < 2.2e-16 ***
## lin1        -4.5005e-09  3.5523e-07 -0.0127    0.9899    
## lin2         1.4173e-06  3.2251e-07  4.3947 1.109e-05 ***
## postchange  -3.0601e-04  2.3720e-04 -1.2901    0.1970    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0027073  -0.0010789  -0.0002822   0.0009302   0.0041919  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -2.203e-04  2.048e-04  -1.075  0.28293   
## lin1         1.385e-06  2.121e-06   0.653  0.51430   
## lin2         6.943e-06  2.102e-06   3.303  0.00106 **
## postchange  -8.542e-05  2.905e-04  -0.294  0.76890   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.777211e-06)
## 
##     Null deviance: 0.00064438  on 336  degrees of freedom
## Residual deviance: 0.00059181  on 333  degrees of freedom
## AIC: -3499.7
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)   
## (Intercept) -2.2025e-04  1.7709e-04 -1.2437 0.213594   
## lin1         1.3846e-06  1.7631e-06  0.7853 0.432269   
## lin2         6.9430e-06  2.3199e-06  2.9928 0.002764 **
## postchange  -8.5417e-05  2.5613e-04 -0.3335 0.738765   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "21 Regression for rate_xbxchib model2 on 2016-12-15/purple/50-90%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0027293  -0.0014475  -0.0002937   0.0011457   0.0073375  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.786e-03  1.391e-04  12.845  < 2e-16 ***
## lin1        -1.238e-06  3.588e-07  -3.450 0.000578 ***
## lin2         5.175e-08  3.580e-07   0.145 0.885091    
## postchange   1.325e-03  1.968e-04   6.733 2.46e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 3.256275e-06)
## 
##     Null deviance: 0.0046946  on 1344  degrees of freedom
## Residual deviance: 0.0043667  on 1341  degrees of freedom
## AIC: -13171
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  1.7864e-03  9.5242e-05 18.7560 < 2.2e-16 ***
## lin1        -1.2381e-06  2.7186e-07 -4.5540 5.264e-06 ***
## lin2         5.1753e-08  3.5101e-07  0.1474    0.8828    
## postchange   1.3251e-03  1.7358e-04  7.6338 2.280e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0019586  -0.0011186  -0.0001618   0.0009957   0.0041645  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.044e-04  2.020e-04   0.517 0.605737    
## lin1         1.953e-06  2.092e-06   0.934 0.351234    
## lin2         1.166e-06  2.074e-06   0.562 0.574398    
## postchange  -9.994e-04  2.865e-04  -3.488 0.000553 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.729419e-06)
## 
##     Null deviance: 0.00062374  on 336  degrees of freedom
## Residual deviance: 0.00057590  on 333  degrees of freedom
## AIC: -3508.9
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  1.0437e-04  1.5504e-04  0.6732 0.5008196    
## lin1         1.9530e-06  1.5822e-06  1.2344 0.2170693    
## lin2         1.1656e-06  1.9575e-06  0.5955 0.5515181    
## postchange  -9.9936e-04  2.5936e-04 -3.8531 0.0001166 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "22 Regression for rate_xbxchib model2 on 2016-11-17/blue/content change"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.002198  -0.001255  -0.000356   0.000935   0.050606  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.777e-03  1.820e-04   9.762  < 2e-16 ***
## lin1        -1.156e-06  4.878e-07  -2.370  0.01791 *  
## lin2        -1.223e-06  4.598e-07  -2.660  0.00791 ** 
## postchange   8.382e-04  2.552e-04   3.285  0.00105 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5.37086e-06)
## 
##     Null deviance: 0.0071371  on 1319  degrees of freedom
## Residual deviance: 0.0070681  on 1316  degrees of freedom
## AIC: -12266
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  1.7767e-03  1.9484e-04  9.1192 < 2.2e-16 ***
## lin1        -1.1564e-06  8.7568e-07 -1.3205 0.1866550    
## lin2        -1.2231e-06  2.7122e-07 -4.5099 6.487e-06 ***
## postchange   8.3824e-04  2.2743e-04  3.6857 0.0002281 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##        Min          1Q      Median          3Q         Max  
## -0.0019125  -0.0011896  -0.0002557   0.0010426   0.0056722  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1.249e-04  2.302e-04   0.542    0.588
## lin1         9.880e-08  2.764e-06   0.036    0.972
## lin2         7.431e-07  2.197e-06   0.338    0.735
## postchange  -4.212e-04  3.152e-04  -1.336    0.182
## 
## (Dispersion parameter for gaussian family taken to be 1.941461e-06)
## 
##     Null deviance: 0.00061169  on 313  degrees of freedom
## Residual deviance: 0.00060185  on 310  degrees of freedom
## AIC: -3232.7
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)
## (Intercept)  1.2486e-04  1.8480e-04  0.6756   0.4993
## lin1         9.8799e-08  2.1492e-06  0.0460   0.9633
## lin2         7.4310e-07  2.0228e-06  0.3674   0.7133
## postchange  -4.2120e-04  2.8979e-04 -1.4534   0.1461

## [1] "23 Regression for xbxcall model2 on 2016-10-11/purple/0-10%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1042.3   -760.0     31.5    617.0   3617.6  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.008e+03  5.691e+01  17.715   <2e-16 ***
## lin1        -9.568e-02  1.491e-01  -0.642    0.521    
## lin2        -5.912e-03  1.461e-01  -0.040    0.968    
## postchange  -4.596e+01  8.031e+01  -0.572    0.567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 537293)
## 
##     Null deviance: 716394165  on 1332  degrees of freedom
## Residual deviance: 714062370  on 1329  degrees of freedom
## AIC: 21377
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  1.0082e+03  5.5785e+01 18.0728   <2e-16 ***
## lin1        -9.5679e-02  1.5058e-01 -0.6354   0.5252    
## lin2        -5.9123e-03  1.3833e-01 -0.0427   0.9659    
## postchange  -4.5957e+01  7.7550e+01 -0.5926   0.5534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1081.62   -721.32     34.45    617.83   1763.92  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   90.625    112.895   0.803    0.423
## lin1           1.300      1.243   1.045    0.297
## lin2           1.638      1.124   1.457    0.146
## postchange  -190.531    157.740  -1.208    0.228
## 
## (Dispersion parameter for gaussian family taken to be 508234)
## 
##     Null deviance: 166003861  on 326  degrees of freedom
## Residual deviance: 164159596  on 323  degrees of freedom
## AIC: 5230.3
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)   90.6250   114.7811  0.7895   0.4298
## lin1           1.3001     1.2515  1.0388   0.2989
## lin2           1.6376     1.1029  1.4848   0.1376
## postchange  -190.5308   152.7118 -1.2476   0.2122

## [1] "24 Regression for xbxcall model2 on 2016-10-18/purple/10-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1048.1   -741.4     21.1    603.7   3584.7  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 960.28947   55.82846  17.201   <2e-16 ***
## lin1         -0.20996    0.14623  -1.436    0.151    
## lin2         -0.03477    0.15100  -0.230    0.818    
## postchange  -16.20591   79.47070  -0.204    0.838    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 517002.9)
## 
##     Null deviance: 679372762  on 1309  degrees of freedom
## Residual deviance: 675205830  on 1306  degrees of freedom
## AIC: 20958
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 960.289470  58.507021 16.4132   <2e-16 ***
## lin1         -0.209965   0.168604 -1.2453   0.2130    
## lin2         -0.034774   0.142015 -0.2449   0.8066    
## postchange  -16.205912  79.791012 -0.2031   0.8391    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1062.55   -727.14     27.27    619.67   1771.03  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  154.363    109.494   1.410    0.160
## lin1           1.646      1.134   1.451    0.148
## lin2           1.293      1.144   1.130    0.259
## postchange  -211.034    155.781  -1.355    0.176
## 
## (Dispersion parameter for gaussian family taken to be 508045.8)
## 
##     Null deviance: 169984586  on 334  degrees of freedom
## Residual deviance: 168163147  on 331  degrees of freedom
## AIC: 5358
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  154.3634   114.2789  1.3508   0.1768
## lin1           1.6458     1.1081  1.4852   0.1375
## lin2           1.2928     1.1539  1.1204   0.2625
## postchange  -211.0344   158.5626 -1.3309   0.1832

## [1] "25 Regression for xbxcall model2 on 2016-11-01/purple/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -994.1  -733.8    14.6   594.5  1801.0  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 889.6170    53.6200  16.591   <2e-16 ***
## lin1         -0.2136     0.1409  -1.516   0.1297    
## lin2          0.2411     0.1441   1.673   0.0946 .  
## postchange   -6.9056    76.2092  -0.091   0.9278    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 475471.1)
## 
##     Null deviance: 623389131  on 1309  degrees of freedom
## Residual deviance: 620965201  on 1306  degrees of freedom
## AIC: 20848
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 889.61699   49.98253 17.7986   <2e-16 ***
## lin1         -0.21359    0.13559 -1.5752   0.1152    
## lin2          0.24112    0.15436  1.5621   0.1183    
## postchange   -6.90557   74.47389 -0.0927   0.9261    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1079.94   -697.82     61.97    553.81   1384.07  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -95.1894   101.2375  -0.940    0.348
## lin1         -0.5197     1.0484  -0.496    0.620
## lin2          1.4090     1.0391   1.356    0.176
## postchange   74.6513   143.5982   0.520    0.604
## 
## (Dispersion parameter for gaussian family taken to be 434314.2)
## 
##     Null deviance: 147453366  on 336  degrees of freedom
## Residual deviance: 144626625  on 333  degrees of freedom
## AIC: 5337.1
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -95.18940   90.61505 -1.0505   0.2935
## lin1         -0.51967    0.98406 -0.5281   0.5974
## lin2          1.40898    1.05874  1.3308   0.1833
## postchange   74.65127  136.89999  0.5453   0.5855

## [1] "26 Regression for xbxcall model2 on 2016-12-15/purple/50-90%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1312.5   -852.7     -6.4    632.4   4494.8  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.021e+03  7.098e+01  14.381  < 2e-16 ***
## lin1        1.219e-01  1.831e-01   0.665  0.50586    
## lin2        4.927e-02  1.827e-01   0.270  0.78748    
## postchange  2.934e+02  1.005e+02   2.920  0.00355 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 848233.3)
## 
##     Null deviance: 1179308771  on 1344  degrees of freedom
## Residual deviance: 1137480856  on 1341  degrees of freedom
## AIC: 22183
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value  Pr(>|z|)    
## (Intercept) 1.0207e+03 5.3691e+01 19.0115 < 2.2e-16 ***
## lin1        1.2188e-01 1.3561e-01  0.8987  0.368803    
## lin2        4.9272e-02 1.6865e-01  0.2922  0.770169    
## postchange  2.9335e+02 8.9570e+01  3.2751  0.001056 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1153.97   -721.68     89.42    629.24   1702.38  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  183.367    110.068   1.666 0.096666 .  
## lin1           2.705      1.140   2.373 0.018199 *  
## lin2           1.743      1.130   1.543 0.123746    
## postchange  -585.360    156.123  -3.749 0.000209 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 513383.7)
## 
##     Null deviance: 178831913  on 336  degrees of freedom
## Residual deviance: 170956761  on 333  degrees of freedom
## AIC: 5393.5
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value  Pr(>|z|)    
## (Intercept)  183.3665   114.6236  1.5997 0.1096592    
## lin1           2.7052     1.0836  2.4965 0.0125438 *  
## lin2           1.7434     1.1487  1.5176 0.1291038    
## postchange  -585.3603   158.3803 -3.6959 0.0002191 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "27 Regression for xbxcall model2 on 2016-11-17/blue/content change"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -981.16  -719.94    35.93   586.69  1732.03  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 910.83446   53.72746  16.953   <2e-16 ***
## lin1         -0.04405    0.14400  -0.306    0.760    
## lin2          0.13445    0.13573   0.991    0.322    
## postchange   25.18241   75.32866   0.334    0.738    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 467997.3)
## 
##     Null deviance: 617431534  on 1319  degrees of freedom
## Residual deviance: 615884394  on 1316  degrees of freedom
## AIC: 20986
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 910.834464  51.474188 17.6950   <2e-16 ***
## lin1         -0.044052   0.141241 -0.3119   0.7551    
## lin2          0.134447   0.135551  0.9919   0.3213    
## postchange   25.182411  73.366660  0.3432   0.7314    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1015.61   -687.32     71.64    596.81   1209.10  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   59.960    105.781   0.567   0.5712  
## lin1           1.042      1.270   0.820   0.4127  
## lin2           1.790      1.009   1.773   0.0772 .
## postchange  -234.585    144.833  -1.620   0.1063  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 409834.9)
## 
##     Null deviance: 128616976  on 313  degrees of freedom
## Residual deviance: 127048809  on 310  degrees of freedom
## AIC: 4955
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   59.9602   106.7762  0.5616  0.57442  
## lin1           1.0419     1.2366  0.8426  0.39948  
## lin2           1.7895     1.0069  1.7773  0.07552 .
## postchange  -234.5853   142.0983 -1.6509  0.09877 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "28 Regression for rate_xbxcall model2 on 2016-10-11/purple/0-10%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.00907  -0.00639  -0.00231   0.00453   0.39687  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.458e-03  1.238e-03   6.833 1.26e-11 ***
## lin1         8.878e-07  3.242e-06   0.274    0.784    
## lin2        -8.033e-07  3.177e-06  -0.253    0.800    
## postchange   1.021e-03  1.747e-03   0.585    0.559    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0002541588)
## 
##     Null deviance: 0.33818  on 1332  degrees of freedom
## Residual deviance: 0.33778  on 1329  degrees of freedom
## AIC: -7245.1
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  8.4584e-03  5.1585e-04 16.3970   <2e-16 ***
## lin1         8.8783e-07  1.3373e-06  0.6639   0.5068    
## lin2        -8.0335e-07  2.0697e-06 -0.3881   0.6979    
## postchange   1.0213e-03  1.5056e-03  0.6783   0.4976    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.009667  -0.005786  -0.001411   0.005303   0.017423  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.919e-03  1.038e-03   1.849 0.065404 .  
## lin1         2.328e-05  1.143e-05   2.037 0.042503 *  
## lin2         2.481e-05  1.033e-05   2.401 0.016934 *  
## postchange  -5.397e-03  1.450e-03  -3.722 0.000233 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.294894e-05)
## 
##     Null deviance: 0.014472  on 326  degrees of freedom
## Residual deviance: 0.013873  on 323  degrees of freedom
## AIC: -2354.2
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  1.9187e-03  1.0997e-03  1.7448   0.08102 .  
## lin1         2.3281e-05  1.1155e-05  2.0870   0.03688 *  
## lin2         2.4806e-05  9.6420e-06  2.5727   0.01009 *  
## postchange  -5.3972e-03  1.3545e-03 -3.9847 6.757e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "29 Regression for rate_xbxcall model2 on 2016-10-18/purple/10-30%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.01101  -0.00628  -0.00215   0.00455   0.39526  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.240e-03  1.243e-03   6.631 4.87e-11 ***
## lin1         3.398e-07  3.255e-06   0.104   0.9169    
## lin2        -7.543e-06  3.361e-06  -2.244   0.0250 *  
## postchange   3.344e-03  1.769e-03   1.890   0.0589 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0002561539)
## 
##     Null deviance: 0.33616  on 1309  degrees of freedom
## Residual deviance: 0.33454  on 1306  degrees of freedom
## AIC: -7109.7
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)    
## (Intercept)  8.2400e-03  5.2875e-04 15.5839   <2e-16 ***
## lin1         3.3977e-07  1.4420e-06  0.2356   0.8137    
## lin2        -7.5425e-06  5.5748e-06 -1.3530   0.1761    
## postchange   3.3440e-03  2.6737e-03  1.2507   0.2110    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.01552  -0.00769  -0.00309   0.00297   0.39254  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1.591e-03  4.553e-03   0.349    0.727
## lin1         2.184e-05  4.715e-05   0.463    0.644
## lin2         4.740e-05  4.758e-05   0.996    0.320
## postchange  -3.279e-03  6.478e-03  -0.506    0.613
## 
## (Dispersion parameter for gaussian family taken to be 0.0008785053)
## 
##     Null deviance: 0.29238  on 334  degrees of freedom
## Residual deviance: 0.29079  on 331  degrees of freedom
## AIC: -1400.8
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)   
## (Intercept)  1.5909e-03  1.0769e-03  1.4773 0.139587   
## lin1         2.1842e-05  9.6498e-06  2.2635 0.023607 * 
## lin2         4.7399e-05  1.7998e-05  2.6336 0.008448 **
## postchange  -3.2793e-03  2.6919e-03 -1.2182 0.223147   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "30 Regression for rate_xbxcall model2 on 2016-11-01/purple/30-50%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.00952  -0.00636  -0.00209   0.00449   0.39682  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.729e-03  1.251e-03   7.777  1.5e-14 ***
## lin1         1.706e-06  3.287e-06   0.519    0.604    
## lin2         4.397e-06  3.363e-06   1.307    0.191    
## postchange  -2.577e-03  1.778e-03  -1.449    0.147    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0002588406)
## 
##     Null deviance: 0.33867  on 1309  degrees of freedom
## Residual deviance: 0.33805  on 1306  degrees of freedom
## AIC: -7096.1
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  9.7293e-03  1.5228e-03  6.3893 1.667e-10 ***
## lin1         1.7064e-06  2.3358e-06  0.7305  0.465065    
## lin2         4.3965e-06  1.5108e-06  2.9102  0.003612 ** 
## postchange  -2.5774e-03  1.6113e-03 -1.5995  0.109707    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.010363  -0.005432  -0.001211   0.004841   0.015513  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -2.563e-03  9.430e-04  -2.718  0.00691 **
## lin1         3.899e-07  9.766e-06   0.040  0.96818   
## lin2         2.518e-05  9.680e-06   2.602  0.00969 **
## postchange   1.591e-03  1.338e-03   1.190  0.23505   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 3.768637e-05)
## 
##     Null deviance: 0.013999  on 336  degrees of freedom
## Residual deviance: 0.012550  on 333  degrees of freedom
## AIC: -2470.4
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)   
## (Intercept) -2.5631e-03  8.0802e-04 -3.1721 0.001513 **
## lin1         3.8993e-07  8.2985e-06  0.0470 0.962523   
## lin2         2.5183e-05  1.0129e-05  2.4863 0.012908 * 
## postchange   1.5913e-03  1.1830e-03  1.3451 0.178583   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "31 Regression for rate_xbxcall model2 on 2016-12-15/purple/50-90%"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.012949  -0.007363  -0.001841   0.005994   0.065171  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.092e-03  7.195e-04  11.247  < 2e-16 ***
## lin1        -1.513e-06  1.857e-06  -0.815    0.415    
## lin2        -1.522e-06  1.852e-06  -0.822    0.411    
## postchange   5.338e-03  1.018e-03   5.242 1.85e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 8.717115e-05)
## 
##     Null deviance: 0.12328  on 1344  degrees of freedom
## Residual deviance: 0.11690  on 1341  degrees of freedom
## AIC: -8749.6
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  8.0922e-03  4.8272e-04 16.7636 < 2.2e-16 ***
## lin1        -1.5127e-06  1.3142e-06 -1.1511    0.2497    
## lin2        -1.5224e-06  1.7388e-06 -0.8755    0.3813    
## postchange   5.3377e-03  8.9783e-04  5.9451 2.763e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.010145  -0.006077  -0.001046   0.006201   0.017045  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.882e-04  1.048e-03   0.847 0.397371    
## lin1         1.385e-05  1.085e-05   1.276 0.202792    
## lin2         1.336e-05  1.076e-05   1.242 0.215278    
## postchange  -5.574e-03  1.487e-03  -3.749 0.000209 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.655516e-05)
## 
##     Null deviance: 0.016558  on 336  degrees of freedom
## Residual deviance: 0.015503  on 333  degrees of freedom
## AIC: -2399.2
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  8.8822e-04  9.2815e-04  0.9570    0.3386    
## lin1         1.3852e-05  9.0405e-06  1.5322    0.1255    
## lin2         1.3357e-05  1.0755e-05  1.2420    0.2142    
## postchange  -5.5735e-03  1.4116e-03 -3.9482 7.873e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## [1] "32 Regression for rate_xbxcall model2 on 2016-11-17/blue/content change"
## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.01198  -0.00639  -0.00184   0.00462   0.39406  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.689e-03  1.254e-03   4.536 6.24e-06 ***
## lin1        -1.062e-05  3.361e-06  -3.160  0.00161 ** 
## lin2        -1.382e-06  3.168e-06  -0.436  0.66281    
## postchange   3.391e-03  1.758e-03   1.928  0.05402 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0002549532)
## 
##     Null deviance: 0.33820  on 1319  degrees of freedom
## Residual deviance: 0.33552  on 1316  degrees of freedom
## AIC: -7170.3
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value  Pr(>|z|)    
## (Intercept)  5.6887e-03  1.3813e-03  4.1184 3.815e-05 ***
## lin1        -1.0622e-05  6.6593e-06 -1.5951   0.11070    
## lin2        -1.3817e-06  1.3151e-06 -1.0507   0.29342    
## postchange   3.3905e-03  1.4852e-03  2.2829   0.02243 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## glm(formula = form, data = df)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.008306  -0.005659  -0.001179   0.005061   0.031560  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.782e-03  1.055e-03   1.689   0.0923 .
## lin1         8.608e-06  1.267e-05   0.679   0.4974  
## lin2         9.171e-06  1.007e-05   0.911   0.3632  
## postchange  -3.279e-03  1.445e-03  -2.270   0.0239 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.078777e-05)
## 
##     Null deviance: 0.012973  on 313  degrees of freedom
## Residual deviance: 0.012644  on 310  degrees of freedom
## AIC: -2276.6
## 
## Number of Fisher Scoring iterations: 2
## 
## 
## z test of coefficients:
## 
##                Estimate  Std. Error z value Pr(>|z|)  
## (Intercept)  1.7822e-03  9.1340e-04  1.9512  0.05103 .
## lin1         8.6079e-06  1.0756e-05  0.8003  0.42356  
## lin2         9.1706e-06  9.7077e-06  0.9447  0.34483  
## postchange  -3.2793e-03  1.3732e-03 -2.3881  0.01694 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Results

Step 1 Results

idx title model formula chgDate chgVal aic deviance
1 SMC - Hourly Chats model2 winchib ~ lin1 + lin2 + postchange 2016-08-17 0_30 16460.222 1.614176e+07
2 SMC - Hourly Chats model2 winchib ~ lin1 + lin2 + postchange 2016-09-1 30_50 16682.477 1.904215e+07
3 SMC - Hourly Chats model2 winchib ~ lin1 + lin2 + postchange 2016-09-07 50_100 16735.702 1.981081e+07
4 SMC - Hourly Chat Rate per Session model2 rate_winchib ~ lin1 + lin2 + postchange 2016-08-17 0_30 -15450.044 8.022000e-04
5 SMC - Hourly Chat Rate per Session model2 rate_winchib ~ lin1 + lin2 + postchange 2016-09-1 30_50 -15301.081 8.961000e-04
6 SMC - Hourly Chat Rate per Session model2 rate_winchib ~ lin1 + lin2 + postchange 2016-09-07 50_100 -15236.116 9.405000e-04
7 SMC - Hourly Chats model2 wincall ~ lin1 + lin2 + postchange 2016-08-17 0_30 18983.065 1.053333e+08
8 SMC - Hourly Chats model2 wincall ~ lin1 + lin2 + postchange 2016-09-1 30_50 18795.588 9.162844e+07
9 SMC - Hourly Chats model2 wincall ~ lin1 + lin2 + postchange 2016-09-07 50_100 18869.232 9.678534e+07
10 SMC - Hourly Chat Rate per Session model2 rate_wincall ~ lin1 + lin2 + postchange 2016-08-17 0_30 -13029.652 4.850700e-03
11 SMC - Hourly Chat Rate per Session model2 rate_wincall ~ lin1 + lin2 + postchange 2016-09-1 30_50 -13405.499 3.668100e-03
12 SMC - Hourly Chat Rate per Session model2 rate_wincall ~ lin1 + lin2 + postchange 2016-09-07 50_100 -13364.476 3.781700e-03
13 Xbox - Hourly Chats model2 xbxchib ~ lin1 + lin2 + postchange 2016-10-11 0_10 16763.621 2.242458e+07
14 Xbox - Hourly Chats model2 xbxchib ~ lin1 + lin2 + postchange 2016-10-18 10_30 16344.307 1.995205e+07
15 Xbox - Hourly Chats model2 xbxchib ~ lin1 + lin2 + postchange 2016-11-01 30_50 16209.956 1.800724e+07
16 Xbox - Hourly Chats model2 xbxchib ~ lin1 + lin2 + postchange 2016-12-15 50_90 17322.618 3.064889e+07
17 Xbox - Hourly Chats model2 xbxchib ~ lin1 + lin2 + postchange 2016-11-17 content change 16342.355 1.826516e+07
18 Xbox - Hourly Chat Rate per Session model2 rate_xbxchib ~ lin1 + lin2 + postchange 2016-10-11 0_10 -12363.562 7.261400e-03
19 Xbox - Hourly Chat Rate per Session model2 rate_xbxchib ~ lin1 + lin2 + postchange 2016-10-18 10_30 -12178.442 6.983200e-03
20 Xbox - Hourly Chat Rate per Session model2 rate_xbxchib ~ lin1 + lin2 + postchange 2016-11-01 30_50 -12165.739 7.051200e-03
21 Xbox - Hourly Chat Rate per Session model2 rate_xbxchib ~ lin1 + lin2 + postchange 2016-12-15 50_90 -13171.038 4.366700e-03
22 Xbox - Hourly Chat Rate per Session model2 rate_xbxchib ~ lin1 + lin2 + postchange 2016-11-17 content change -12265.578 7.068100e-03
23 Xbox - Hourly Chats model2 xbxcall ~ lin1 + lin2 + postchange 2016-10-11 0_10 21376.884 7.140624e+08
24 Xbox - Hourly Chats model2 xbxcall ~ lin1 + lin2 + postchange 2016-10-18 10_30 20957.716 6.752058e+08
25 Xbox - Hourly Chats model2 xbxcall ~ lin1 + lin2 + postchange 2016-11-01 30_50 20848.013 6.209652e+08
26 Xbox - Hourly Chats model2 xbxcall ~ lin1 + lin2 + postchange 2016-12-15 50_90 22183.414 1.137481e+09
27 Xbox - Hourly Chats model2 xbxcall ~ lin1 + lin2 + postchange 2016-11-17 content change 20986.199 6.158844e+08
28 Xbox - Hourly Chat Rate per Session model2 rate_xbxcall ~ lin1 + lin2 + postchange 2016-10-11 0_10 -7245.092 3.377771e-01
29 Xbox - Hourly Chat Rate per Session model2 rate_xbxcall ~ lin1 + lin2 + postchange 2016-10-18 10_30 -7109.736 3.345370e-01
30 Xbox - Hourly Chat Rate per Session model2 rate_xbxcall ~ lin1 + lin2 + postchange 2016-11-01 30_50 -7096.068 3.380459e-01
31 Xbox - Hourly Chat Rate per Session model2 rate_xbxcall ~ lin1 + lin2 + postchange 2016-12-15 50_90 -8749.633 1.168965e-01
32 Xbox - Hourly Chat Rate per Session model2 rate_xbxcall ~ lin1 + lin2 + postchange 2016-11-17 content change -7170.257 3.355184e-01

Step 2 Results

idx title model formula chgDate chgVal chgCoef.fit chgCoef.smry chgStd.smry chgTval.smry chgPval.smry s
1 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-08-17 0_30 9.6937243 9.6937243 25.4228096 0.3813003 0.7032236 |
2 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-1 30_50 -19.8954312 -19.8954312 24.3552471 -0.8168848 0.4145783 |
3 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-07 50_100 30.8848242 30.8848242 24.7849350 1.2461128 0.2135989 |
4 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-08-17 0_30 0.0001103 0.0001103 0.0001694 0.6509015 0.5155589 |
5 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-1 30_50 0.0000412 0.0000412 0.0001746 0.2357684 0.8137573 |
6 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-07 50_100 0.0000515 0.0000515 0.0001785 0.2883932 0.7732252 |
7 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-08-17 0_30 -63.1475081 -63.1475081 56.5905347 -1.1158670 0.2652837 |
8 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-1 30_50 -30.3619277 -30.3619277 51.4377453 -0.5902655 0.5554130 |
9 SMC - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-07 50_100 -2.3885621 -2.3885621 50.0627327 -0.0477114 0.9619749 |
10 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-08-17 0_30 -0.0004794 -0.0004794 0.0003526 -1.3594917 0.1749107 |
11 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-1 30_50 -0.0001361 -0.0001361 0.0003679 -0.3700223 0.7116011 |
12 SMC - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-09-07 50_100 -0.0003447 -0.0003447 0.0003585 -0.9615609 0.3369679 |
13 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-11 0_10 -22.0893426 -22.0893426 25.1464294 -0.8784286 0.3803641 |
14 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-18 10_30 -44.0174327 -44.0174327 24.0911387 -1.8271213 0.0685819 | .
15 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-01 30_50 -6.2570898 -6.2570898 24.6679937 -0.2536522 0.7999208 |
16 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-12-15 50_90 -98.4642598 -98.4642598 24.5202226 -4.0156348 0.0000733 | ***
17 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-17 content change -43.4399858 -43.4399858 26.9109499 -1.6142123 0.1074988 |
18 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-11 0_10 -0.0009049 -0.0009049 0.0002812 -3.2182581 0.0014206 | **
19 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-18 10_30 -0.0007128 -0.0007128 0.0008525 -0.8361953 0.4036483 |
20 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-01 30_50 -0.0000854 -0.0000854 0.0002905 -0.2940537 0.7689001 |
21 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-12-15 50_90 -0.0009994 -0.0009994 0.0002865 -3.4875733 0.0005528 | ***
22 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-17 content change -0.0004212 -0.0004212 0.0003152 -1.3361586 0.1824774 |
23 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-11 0_10 -190.5308297 -190.5308297 157.7400158 -1.2078789 0.2279775 |
24 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-18 10_30 -211.0343513 -211.0343513 155.7808819 -1.3546871 0.1764412 |
25 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-01 30_50 74.6512694 74.6512694 143.5981888 0.5198622 0.6035050 |
26 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-12-15 50_90 -585.3603018 -585.3603018 156.1233916 -3.7493440 0.0002090 | ***
27 Xbox - Hourly Chats model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-17 content change -234.5853205 -234.5853205 144.8325824 -1.6196999 0.1063136 |
28 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-11 0_10 -0.0053972 -0.0053972 0.0014501 -3.7220550 0.0002330 | ***
29 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-10-18 10_30 -0.0032793 -0.0032793 0.0064779 -0.5062257 0.6130352 |
30 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-01 30_50 0.0015913 0.0015913 0.0013376 1.1895974 0.2350522 |
31 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-12-15 50_90 -0.0055735 -0.0055735 0.0014867 -3.7488508 0.0002094 | ***
32 Xbox - Hourly Chat Rate per Session model2 step1residuals ~ lin1 + lin2 + postchange 2016-11-17 content change -0.0032793 -0.0032793 0.0014449 -2.2696579 0.0239149 | *

Done

## [1] "Version 0.1 created on 24 Mar 2017 - 16:55:10 took 85.8 secs"